Abstract

The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden.The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented. The development of simulation/reconstruction algorithms in this context poses three main difficulties. First, the algorithms deal with large data volumes and are computationally expensive, thus leading to the need for hardware and software optimizations. Second, these optimizations are limited by the high flexibility required to explore new scanning geometries, including fully configurable positioning of source and detector elements. And third, the evolution of the various hardware setups increases the effort required for maintaining and adapting the implementations to current and future programming models. Previous works lack support for completely flexible geometries and/or compatibility with multiple programming models and platforms.In this paper, we present FUX-Sim, a novel X-ray simulation/reconstruction framework that was designed to be flexible and fast. Optimized implementation for different families of GPUs (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures.A detailed performance evaluation demonstrates that for different system configurations and hardware platforms, FUX-Sim maximizes performance with the CUDA programming model (5 times faster than other state-of-the-art implementations). Furthermore, the CPU and OpenCL programming models allow FUX-Sim to be executed over a wide range of hardware platforms.

Highlights

  • In recent decades, there has been a rapid advance towards the use of digital equipment in radiology

  • Optimized implementation for different families of graphic processor units (GPUs) (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures

  • More recent works have opted for graphic processor units (GPUs), with CUDA and OpenCL being the most widely used programming models [4]

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Summary

Introduction

There has been a rapid advance towards the use of digital equipment in radiology. Research on new configurations for X-ray systems, new acquisition protocols, and advanced reconstruction algorithms to obtain tomographic images from a limited number of projections can benefit from simulation tools, which enable evaluation of possibilities before their actual implementation in real systems. CT Sim [1] is an open source CT simulator that enables the projection of various phantoms, it is limited to 2D circular scans with ideal parallel-beam and fan-beam geometries It provides analytical reconstruction methods (FBP and Direct Fourier), without supporting iterative reconstruction algorithms. Given the high computational burden of some of the algorithms used in simulation and reconstruction, it is widely accepted that parallel implementations are needed to achieve reasonable execution times Along these lines, more recent works have opted for graphic processor units (GPUs), with CUDA and OpenCL being the most widely used programming models [4]. We detail the optimizations carried out at each layer in terms of computation and memory management and evaluate different system setups by comparing three programming models

General description of the FUX-Sim framework
Kernel layer
Projection kernel
Backprojection kernel
Optimizations
Processing operations
Platform management kernels
Architecture layer
Cone-beam with circular trajectory
Helical scan
Arbitrary positioning
Tomosynthesis
Wide field of view
Evaluation
Discussion
Full Text
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